Benchmark: lodash with flatmap and groupby vs native

HTML Preparation code:

<script src="https://cdnjs.cloudflare.com/ajax/libs/lodash.js/4.17.4/lodash.min.js"></script>

Script Preparation code:

window.normalizationRules = [];

for (var i = 0, len = 1000; i < len; i++) {
  const attributes = [];
  for (var n = 0, len = 1000; n < len; n++) {
   attributes.push({id: n, value: n});
  }
  window.normalizationRules.push({attributes});
}

Tests:

lodash
_(normalizationRules).flatMap(r => r.attributes).groupBy(a => a.id).value();
reduce
const normalizationRulesByAttributeId = normalizationRules.reduce((acc, rule) => { rule.attributes.forEach((a) => { if (!acc[a.id]) { acc[a.id] = []; } acc[a.id].push(rule); }); return acc; }, {});

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
lodash
reduce

Fastest: N/A

Slowest: N/A

Latest run results:

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Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):

LLMs can make mistakes. Check important info.

Let's break down the provided benchmark and its various components.

**Benchmark Definition JSON**

The benchmark is defined by two separate test cases:

1.  **"lodash with flatmap and groupby vs native"**: This test case compares the performance of using Lodash (a popular JavaScript utility library) to perform a specific operation, versus doing it natively without any external libraries.
2.  **"reduce"**: This test case measures the performance of the built-in `Array.prototype.reduce()` method.

**Script Preparation Code**

The script preparation code is used to prepare the benchmark environment by:

*   Creating an array `normalizationRules` with a nested structure, where each element has an `id` and a `value`.
*   Pushing this array into the global scope (`window.normalizationRules`).

This allows the test cases to access the prepared data.

**Html Preparation Code**

The HTML preparation code includes a reference to Lodash 4.17.4, which is loaded from a CDN.

**Individual Test Cases**

There are two individual test cases:

1.  **"lodash"`**: This test case uses Lodash's `flatMap` and `groupBy` methods on the `normalizationRules` array.
2.  **"reduce"`**: This test case uses the built-in `Array.prototype.reduce()` method to achieve a similar result as Lodash.

**Library: Lodash**

Lodash is a popular JavaScript utility library that provides a collection of functional programming helpers, including:

*   `flatMap`: Flattens an array using a provided function.
*   `groupBy`: Groups an array by a specified key function.
*   `reduce`: Reduces an array to a single value.

**Special JS Feature/ Syntax**

None are mentioned in this specific benchmark. However, Lodash does utilize some advanced JavaScript features, such as:

*   **Closures**: Some of its functions use closures to capture and manipulate context.
*   **Higher-Order Functions**: Many of its functions are higher-order, meaning they accept other functions as arguments or return functions.

**Pros and Cons**

Here's a brief overview of the pros and cons of using Lodash vs. doing it natively:

**Lodash:**

Pros:

*   **Convenience**: Provides a wide range of utility methods to simplify common tasks.
*   **Efficiency**: Can optimize certain operations, such as array manipulation.

Cons:

*   **Overhead**: Includes an additional library that needs to be loaded and initialized.
*   **Performance Overhead**: Can introduce performance penalties due to the overhead of the Lodash library.

**Native Approach:**

Pros:

*   **No Overhead**: Does not include any additional libraries or performance overhead.
*   **Optimized Performance**: Can potentially outperform Lodash due to optimized native implementations.

Cons:

*   **Complexity**: Requires more manual effort and expertise in implementing efficient solutions.

**Other Alternatives**

If you're looking for alternatives to Lodash, some popular options include:

*   **Underscore.js**: Another popular utility library with similar functionality.
*   **Ramda**: A functional programming library that provides a different set of utility methods.
*   **Built-in Array Methods**: JavaScript's built-in array methods (e.g., `map`, `filter`, `reduce`) can often be used to achieve similar results.

Ultimately, the choice between using Lodash or doing it natively depends on your specific use case, performance requirements, and personal preference.

Related benchmarks:

lodash with flatmap and groupby vs native (version: 0)

Comparing performance of: lodash vs reduce

Created: 4 years ago by: Guest

Jump to the latest result

lodash

reduce

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

No previous run results

Autogenerated LLM Summary (model llama3.2:3b, generated one year ago):